Tableau-CRM-and-Einstein-Discovery-Consultant Exam Dumps - Salesforce Tableau CRM Einstein Discovery Consultant (SU24)

To potentially improve an AUC score of .62 in a Tableau CRM story, consider the following strategies:

• A. Segment the training set: By segmenting the training set, you can ensure that the model trains on more homogeneously grouped data, which might help in improving the model’s predictive accuracy and, consequently, the AUC score.
• D. Add more rows of relevant data: Increasing the volume of relevant data can provide the model with more examples to learn from, which might enhance its ability to generalize and improve the AUC score.

Two benefits of designing using the "Progressive Disclosure" principle are:

• A: Improved ease of use for end users. By only displaying essential information initially and allowing users to access more detailed data as needed, dashboards become less cluttered and more user-friendly.
• D: Better dashboard performance. This design principle can also lead to better performance as less data is processed and rendered initially, speeding up load times and responsiveness.

In Salesforce Analytics Query Language (SAQL), a keyword refers to specific commands or functions such as 'group', 'foreach', 'filter', and more. These keywords are essential for structuring queries and must be used in lowercase to be recognized by the system. Keywords control operations on data streams within the queries, facilitating complex data manipulations and analyses.

In Tableau CRM (formerly Einstein Analytics), you can restrict access to data at the row level using security predicates. Security predicates are filter expressions added to datasets that dynamically restrict data access based on user attributes or record specifics. This method allows administrators to enforce data security policies directly within the dataset, ensuring that users only see data relevant and permitted to them, without altering the underlying data structure like removing rows manually or adding flags. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security_predicates.htm &type=5

Predicate is a name for a filter condition that defines row-level access to records in a dataset Example: "rowLevelSecurityFilter":"'AccountOwner' == \"$User.Name\""

To analyze data from a CSV file in Einstein Discovery, the correct procedure involves importing the CSV file into an Einstein Analytics dataset and subsequently creating a story from this dataset. This method allows you to fully leverage Einstein Discovery's capabilities to analyze and derive insights from the data presented in the CSV file.